Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Mathematical Programming: Series A and B
Real-time path planning with limited information for autonomous unmanned air vehicles
Automatica (Journal of IFAC)
Flying Fast and Low Among Obstacles: Methodology and Experiments
International Journal of Robotics Research
Vision-based navigation through urban canyons
Journal of Field Robotics
Online world modeling and path planning for an unmanned helicopter
Autonomous Robots
Vision-based local multi-resolution mapping and path planning for miniature air vehicles
ACC'09 Proceedings of the 2009 conference on American Control Conference
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
RANGE–Robust autonomous navigation in GPS-denied environments
Journal of Field Robotics
Autonomous quadrotor flight with vision-based obstacle avoidance in virtual environment
Expert Systems with Applications: An International Journal
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
Multiresolution Hierarchical Path-Planning for Small UAVs Using Wavelet Decompositions
Journal of Intelligent and Robotic Systems
International Journal of Robotics Research
Small Unmanned Aircraft: Theory and Practice
Small Unmanned Aircraft: Theory and Practice
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In this paper we present an observability-based local path planning and obstacle avoidance technique that utilizes an extended Kalman Filter (EKF) to estimate the time-to-collision (TTC) and bearing to obstacles using bearing-only measurements. To ensure that the error covariance matrix computed by an EKF is bounded, the system should be observable. We perform a nonlinear observability analysis to obtain the necessary conditions for complete observability of the system. These conditions are used to explicitly design a path planning algorithm that enhances observability while simultaneously avoiding collisions with obstacles. We analyze the behavior of the path planning algorithm and specially define the environments where the path planning algorithm will guarantee collision-free paths that lead to a goal configuration. Numerical results show the effectiveness of the planning algorithm in solving single and multiple obstacle avoidance problems while improving the estimation accuracy.